Computer system and method for sentiment-based recommendations of discussion topics in social media

Abstract

An online discussion recommendation system ranks and presents to users of a social media site discussions of interest preferably calculated through interest-matching. The discussion has many online posts. The system and method determine which post to present to the user as a sample snippet from such discussion using sentiment analysis. The sentiment of the snippet may be most polarized, sentiment matching the user's current mood, typical user sentiment, or others.

Description

CROSS-REFERENCE TO RELATED APPLICATION(S)[0001]This application claims the benefit of U.S. Provisional Application No. 61 / 430,516 filed Jan. 6, 2011 and entitled “System for Sentiment Based Recommendations of Discussion Topics in Social Media”.[0002]The foregoing patent application is incorporated by reference in its entirety.BACKGROUND[0003]Popular social media sites are attracting millions of users who contribute and consume content. A major activity on those sites besides uploading content (such as photos, videos, bookmarks etc.) is to talk about the shared content. Those discussions are typically represented as comments from different users on the content pages of the site. Sometimes, as in the case of discussion forums, only a topic but no content is associated with the discussion.[0004]Given the number of discussions on those sites, finding interesting and relevant discussions to follow is a challenge. Recommendation systems can help users identify discussions that match their...

AI Technical Summary

Benefits of technology

[0008]The present invention improves the click-through likelihood of users by selecting sample comments from matching discussions that reflect either positive or negative sentiment and, optionally, matches a users sentiment preferences or mood. After creating a rank ordered list of matching discussions (discussion recommendations), the system analyzes the sentiment of each comment in each discussion and assigns a positive or negative sentiment score. Or, optionally the system assigns a score for other sentiment categories (e.g. anger, happiness etc.). The system then creates a respective snippet for each recommendation by selecting one or more sample comments with a high sentiment score. Optionally, the system can also match the sentiment chosen for the snippet with the sentiment profile of a user. For example, if users in the past preferred to read positive discussions, then the system selects positive sentiment scoring comments for the snippet.

[0012]using a sentiment analysis, determining one or more of the online posts of the one discussion to present as a sample snippet of the one discussion, such that sentiment of the snippet improves likelihood of click-through by the given user; and

Problems solved by technology

Given the number of discussions on those sites, finding interesting and relevant discussions to follow is a challenge.

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